Overview

Dataset statistics

Number of variables23
Number of observations161
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory45.8 KiB
Average record size in memory291.1 B

Variable types

Numeric21
Categorical2

Alerts

Market Cap(in B USD) is highly overall correlated with Revenue and 11 other fieldsHigh correlation
Revenue is highly overall correlated with Market Cap(in B USD) and 8 other fieldsHigh correlation
Gross Profit is highly overall correlated with Market Cap(in B USD) and 8 other fieldsHigh correlation
Net Income is highly overall correlated with Market Cap(in B USD) and 13 other fieldsHigh correlation
Earning Per Share is highly overall correlated with Net Income and 4 other fieldsHigh correlation
EBITDA is highly overall correlated with Market Cap(in B USD) and 12 other fieldsHigh correlation
Share Holder Equity is highly overall correlated with Market Cap(in B USD) and 6 other fieldsHigh correlation
Cash Flow from Operating is highly overall correlated with Market Cap(in B USD) and 10 other fieldsHigh correlation
Cash Flow from Investing is highly overall correlated with Market Cap(in B USD) and 6 other fieldsHigh correlation
Cash Flow from Financial Activities is highly overall correlated with Revenue and 4 other fieldsHigh correlation
Current Ratio is highly overall correlated with Debt/Equity Ratio and 2 other fieldsHigh correlation
Debt/Equity Ratio is highly overall correlated with Current RatioHigh correlation
ROE is highly overall correlated with Market Cap(in B USD) and 6 other fieldsHigh correlation
ROA is highly overall correlated with Market Cap(in B USD) and 9 other fieldsHigh correlation
ROI is highly overall correlated with Market Cap(in B USD) and 8 other fieldsHigh correlation
Net Profit Margin is highly overall correlated with Market Cap(in B USD) and 9 other fieldsHigh correlation
Return on Tangible Equity is highly overall correlated with Market Cap(in B USD) and 5 other fieldsHigh correlation
Number of Employees is highly overall correlated with Revenue and 1 other fieldsHigh correlation
Company is highly overall correlated with CategoryHigh correlation
Category is highly overall correlated with Company High correlation
Market Cap(in B USD) has unique valuesUnique
Revenue has unique valuesUnique
Gross Profit has unique valuesUnique
Share Holder Equity has unique valuesUnique
Cash Flow from Investing has unique valuesUnique
ROE has unique valuesUnique
ROA has unique valuesUnique
ROI has unique valuesUnique
Net Profit Margin has unique valuesUnique
Return on Tangible Equity has unique valuesUnique
Debt/Equity Ratio has 8 (5.0%) zerosZeros

Reproduction

Analysis started2023-12-19 08:25:38.087065
Analysis finished2023-12-19 08:28:16.530816
Duration2 minutes and 38.44 seconds
Software versionydata-profiling vv4.6.0
Download configurationconfig.json

Variables

Year
Real number (ℝ)

Distinct15
Distinct (%)9.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2015.6087
Minimum2009
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-19T08:28:16.694902image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum2009
5-th percentile2009
Q12012
median2016
Q32019
95-th percentile2022
Maximum2023
Range14
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.0499597
Coefficient of variation (CV)0.0020092986
Kurtosis-1.1543732
Mean2015.6087
Median Absolute Deviation (MAD)3
Skewness0.00032417049
Sum324513
Variance16.402174
MonotonicityNot monotonic
2023-12-19T08:28:17.003833image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
2018 12
 
7.5%
2017 12
 
7.5%
2016 12
 
7.5%
2015 12
 
7.5%
2014 12
 
7.5%
2022 11
 
6.8%
2021 11
 
6.8%
2020 11
 
6.8%
2019 11
 
6.8%
2013 11
 
6.8%
Other values (5) 46
28.6%
ValueCountFrequency (%)
2009 11
6.8%
2010 11
6.8%
2011 11
6.8%
2012 11
6.8%
2013 11
6.8%
2014 12
7.5%
2015 12
7.5%
2016 12
7.5%
2017 12
7.5%
2018 12
7.5%
ValueCountFrequency (%)
2023 2
 
1.2%
2022 11
6.8%
2021 11
6.8%
2020 11
6.8%
2019 11
6.8%
2018 12
7.5%
2017 12
7.5%
2016 12
7.5%
2015 12
7.5%
2014 12
7.5%

Company
Categorical

HIGH CORRELATION 

Distinct12
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Memory size9.7 KiB
MSFT
15 
NVDA
15 
AAPL
14 
GOOG
14 
AIG
14 
Other values (7)
89 

Length

Max length5
Median length4
Mean length3.7142857
Min length3

Characters and Unicode

Total characters598
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAAPL
2nd rowAAPL
3rd rowAAPL
4th rowAAPL
5th rowAAPL

Common Values

ValueCountFrequency (%)
MSFT 15
9.3%
NVDA 15
9.3%
AAPL 14
8.7%
GOOG 14
8.7%
AIG 14
8.7%
PCG 14
8.7%
MCD 14
8.7%
BCS 14
8.7%
INTC 14
8.7%
AMZN 14
8.7%
Other values (2) 19
11.8%

Length

2023-12-19T08:28:17.351296image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
msft 15
9.3%
nvda 15
9.3%
aapl 14
8.7%
goog 14
8.7%
aig 14
8.7%
pcg 14
8.7%
mcd 14
8.7%
bcs 14
8.7%
intc 14
8.7%
amzn 14
8.7%
Other values (2) 19
11.8%

Most occurring characters

ValueCountFrequency (%)
A 71
11.9%
C 56
 
9.4%
G 56
 
9.4%
P 46
 
7.7%
M 43
 
7.2%
N 43
 
7.2%
S 39
 
6.5%
D 39
 
6.5%
L 33
 
5.5%
T 29
 
4.8%
Other values (9) 143
23.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 598
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 71
11.9%
C 56
 
9.4%
G 56
 
9.4%
P 46
 
7.7%
M 43
 
7.2%
N 43
 
7.2%
S 39
 
6.5%
D 39
 
6.5%
L 33
 
5.5%
T 29
 
4.8%
Other values (9) 143
23.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 598
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 71
11.9%
C 56
 
9.4%
G 56
 
9.4%
P 46
 
7.7%
M 43
 
7.2%
N 43
 
7.2%
S 39
 
6.5%
D 39
 
6.5%
L 33
 
5.5%
T 29
 
4.8%
Other values (9) 143
23.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 598
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 71
11.9%
C 56
 
9.4%
G 56
 
9.4%
P 46
 
7.7%
M 43
 
7.2%
N 43
 
7.2%
S 39
 
6.5%
D 39
 
6.5%
L 33
 
5.5%
T 29
 
4.8%
Other values (9) 143
23.9%

Category
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size9.8 KiB
IT
43 
ELEC
29 
BANK
28 
MANUFACTURING
14 
FOOD
14 
Other values (3)
33 

Length

Max length13
Median length4
Mean length4.6024845
Min length2

Characters and Unicode

Total characters741
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIT
2nd rowIT
3rd rowIT
4th rowIT
5th rowIT

Common Values

ValueCountFrequency (%)
IT 43
26.7%
ELEC 29
18.0%
BANK 28
17.4%
MANUFACTURING 14
 
8.7%
FOOD 14
 
8.7%
LOGI 14
 
8.7%
FINANCE 10
 
6.2%
FINTECH 9
 
5.6%

Length

2023-12-19T08:28:17.667146image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-19T08:28:18.042477image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
it 43
26.7%
elec 29
18.0%
bank 28
17.4%
manufacturing 14
 
8.7%
food 14
 
8.7%
logi 14
 
8.7%
finance 10
 
6.2%
fintech 9
 
5.6%

Most occurring characters

ValueCountFrequency (%)
I 90
12.1%
N 85
11.5%
E 77
10.4%
A 66
8.9%
T 66
8.9%
C 62
8.4%
F 47
 
6.3%
L 43
 
5.8%
O 42
 
5.7%
G 28
 
3.8%
Other values (7) 135
18.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 741
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I 90
12.1%
N 85
11.5%
E 77
10.4%
A 66
8.9%
T 66
8.9%
C 62
8.4%
F 47
 
6.3%
L 43
 
5.8%
O 42
 
5.7%
G 28
 
3.8%
Other values (7) 135
18.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 741
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
I 90
12.1%
N 85
11.5%
E 77
10.4%
A 66
8.9%
T 66
8.9%
C 62
8.4%
F 47
 
6.3%
L 43
 
5.8%
O 42
 
5.7%
G 28
 
3.8%
Other values (7) 135
18.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 741
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I 90
12.1%
N 85
11.5%
E 77
10.4%
A 66
8.9%
T 66
8.9%
C 62
8.4%
F 47
 
6.3%
L 43
 
5.8%
O 42
 
5.7%
G 28
 
3.8%
Other values (7) 135
18.2%

Market Cap(in B USD)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct161
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean335.50739
Minimum0
Maximum2913.28
Zeros1
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-19T08:28:18.373149image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.47
Q140.78
median112.65
Q3356.31
95-th percentile1681.61
Maximum2913.28
Range2913.28
Interquartile range (IQR)315.53

Descriptive statistics

Standard deviation540.06503
Coefficient of variation (CV)1.6096964
Kurtosis7.1403625
Mean335.50739
Median Absolute Deviation (MAD)95
Skewness2.6288958
Sum54016.69
Variance291670.23
MonotonicityNot monotonic
2023-12-19T08:28:18.705530image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2066.94 1
 
0.6%
0.37 1
 
0.6%
81.1 1
 
0.6%
32.53 1
 
0.6%
43.6 1
 
0.6%
34.66 1
 
0.6%
40.78 1
 
0.6%
31.68 1
 
0.6%
45.79 1
 
0.6%
46.21 1
 
0.6%
Other values (151) 151
93.8%
ValueCountFrequency (%)
0 1
0.6%
0.04 1
0.6%
0.37 1
0.6%
1.11 1
0.6%
2.19 1
0.6%
3.39 1
0.6%
3.51 1
0.6%
4.04 1
0.6%
4.47 1
0.6%
4.9 1
0.6%
ValueCountFrequency (%)
2913.28 1
0.6%
2525.08 1
0.6%
2451.23 1
0.6%
2255.97 1
0.6%
2066.94 1
0.6%
1910.26 1
0.6%
1787.73 1
0.6%
1691 1
0.6%
1681.61 1
0.6%
1634.16 1
0.6%

Revenue
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct161
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75862.601
Minimum3326.445
Maximum513983
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-19T08:28:19.080574image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum3326.445
5-th percentile6910
Q122820.4
median45992.04
Q377849
95-th percentile265595
Maximum513983
Range510656.55
Interquartile range (IQR)55028.6

Descriptive statistics

Standard deviation90786.896
Coefficient of variation (CV)1.196728
Kurtosis6.539055
Mean75862.601
Median Absolute Deviation (MAD)25350.04
Skewness2.435343
Sum12213879
Variance8.2422605 × 109
MonotonicityNot monotonic
2023-12-19T08:28:19.446496image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
394328 1
 
0.6%
22138 1
 
0.6%
22744.7 1
 
0.6%
30868.08 1
 
0.6%
30169.69 1
 
0.6%
27947.54 1
 
0.6%
27621.9 1
 
0.6%
28212.33 1
 
0.6%
27162.75 1
 
0.6%
29072.54 1
 
0.6%
Other values (151) 151
93.8%
ValueCountFrequency (%)
3326.445 1
0.6%
3424.859 1
0.6%
3543.309 1
0.6%
3997.93 1
0.6%
4130 1
0.6%
4280.159 1
0.6%
4682 1
0.6%
5010 1
0.6%
6910 1
0.6%
8025 1
0.6%
ValueCountFrequency (%)
513983 1
0.6%
469822 1
0.6%
394328 1
0.6%
386064 1
0.6%
365817 1
0.6%
282836 1
0.6%
280522 1
0.6%
274515 1
0.6%
265595 1
0.6%
260174 1
0.6%

Gross Profit
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct161
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37383.463
Minimum1174.269
Maximum225152
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-19T08:28:19.813099image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum1174.269
5-th percentile3527
Q110396
median19561
Q347722
95-th percentile135620
Maximum225152
Range223977.73
Interquartile range (IQR)37326

Descriptive statistics

Standard deviation41669.094
Coefficient of variation (CV)1.1146397
Kurtosis4.2798469
Mean37383.463
Median Absolute Deviation (MAD)13162
Skewness2.0019788
Sum6018737.6
Variance1.7363134 × 109
MonotonicityNot monotonic
2023-12-19T08:28:20.177120image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
170782 1
 
0.6%
4686 1
 
0.6%
8791.8 1
 
0.6%
30868.08 1
 
0.6%
30169.69 1
 
0.6%
27947.54 1
 
0.6%
27621.9 1
 
0.6%
28212.33 1
 
0.6%
27162.75 1
 
0.6%
29072.54 1
 
0.6%
Other values (151) 151
93.8%
ValueCountFrequency (%)
1174.269 1
0.6%
1176.923 1
0.6%
1409.09 1
0.6%
2056.517 1
0.6%
2226.343 1
0.6%
2268 1
0.6%
2599 1
0.6%
2811 1
0.6%
3527 1
0.6%
4063 1
0.6%
ValueCountFrequency (%)
225152 1
0.6%
197478 1
0.6%
170782 1
0.6%
156633 1
0.6%
152836 1
0.6%
152757 1
0.6%
146698 1
0.6%
146052 1
0.6%
135620 1
0.6%
115856 1
0.6%

Net Income
Real number (ℝ)

HIGH CORRELATION 

Distinct160
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12276.607
Minimum-12244
Maximum99803
Zeros0
Zeros (%)0.0%
Negative25
Negative (%)15.5%
Memory size1.4 KiB
2023-12-19T08:28:20.511500image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum-12244
5-th percentile-2221
Q1844
median4757.8
Q314136
95-th percentile57411
Maximum99803
Range112047
Interquartile range (IQR)13292

Descriptive statistics

Standard deviation19417.421
Coefficient of variation (CV)1.5816602
Kurtosis5.3326304
Mean12276.607
Median Absolute Deviation (MAD)4825.787
Skewness2.2482646
Sum1976533.7
Variance3.7703624 × 108
MonotonicityNot monotonic
2023-12-19T08:28:20.855508image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
631 2
 
1.2%
99803 1
 
0.6%
-2477.073 1
 
0.6%
4946.3 1
 
0.6%
4551 1
 
0.6%
6212.949 1
 
0.6%
8766.263 1
 
0.6%
1959.384 1
 
0.6%
4180.571 1
 
0.6%
1860.711 1
 
0.6%
Other values (150) 150
93.2%
ValueCountFrequency (%)
-12244 1
0.6%
-7656 1
0.6%
-6851 1
0.6%
-6084 1
0.6%
-5973 1
0.6%
-3140 1
0.6%
-2722 1
0.6%
-2477.073 1
0.6%
-2221 1
0.6%
-1682 1
0.6%
ValueCountFrequency (%)
99803 1
0.6%
94680 1
0.6%
76033 1
0.6%
72738 1
0.6%
72361 1
0.6%
61271 1
0.6%
59972 1
0.6%
59531 1
0.6%
57411 1
0.6%
55256 1
0.6%

Earning Per Share
Real number (ℝ)

HIGH CORRELATION 

Distinct156
Distinct (%)96.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.98288571
Minimum-90.48
Maximum14.98
Zeros0
Zeros (%)0.0%
Negative25
Negative (%)15.5%
Memory size1.4 KiB
2023-12-19T08:28:21.425297image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum-90.48
5-th percentile-8.78
Q10.34
median1.79
Q33.24
95-th percentile8.05
Maximum14.98
Range105.46
Interquartile range (IQR)2.9

Descriptive statistics

Standard deviation8.874504
Coefficient of variation (CV)9.0290294
Kurtosis71.660862
Mean0.98288571
Median Absolute Deviation (MAD)1.45
Skewness-7.3254644
Sum158.2446
Variance78.756822
MonotonicityNot monotonic
2023-12-19T08:28:22.595413image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.09 2
 
1.2%
5.61 2
 
1.2%
2.13 2
 
1.2%
1.99 2
 
1.2%
2.1 2
 
1.2%
0.5584 1
 
0.6%
1.4744 1
 
0.6%
2.0131 1
 
0.6%
0.4416 1
 
0.6%
0.72 1
 
0.6%
Other values (146) 146
90.7%
ValueCountFrequency (%)
-90.48 1
0.6%
-29.4 1
0.6%
-20.78 1
0.6%
-15.82 1
0.6%
-14.5 1
0.6%
-13.25 1
0.6%
-12.87 1
0.6%
-10.59 1
0.6%
-8.78 1
0.6%
-6.88 1
0.6%
ValueCountFrequency (%)
14.98 1
0.6%
13.01 1
0.6%
11.01 1
0.6%
10.82 1
0.6%
10.04 1
0.6%
9.68 1
0.6%
9.65 1
0.6%
8.33 1
0.6%
8.05 1
0.6%
7.88 1
0.6%

EBITDA
Real number (ℝ)

HIGH CORRELATION 

Distinct148
Distinct (%)91.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20174.024
Minimum-6860
Maximum130541
Zeros0
Zeros (%)0.0%
Negative9
Negative (%)5.6%
Memory size1.4 KiB
2023-12-19T08:28:23.937257image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum-6860
5-th percentile-8
Q12310
median9958
Q327765
95-th percentile81602
Maximum130541
Range137401
Interquartile range (IQR)25455

Descriptive statistics

Standard deviation26342.03
Coefficient of variation (CV)1.30574
Kurtosis3.5065099
Mean20174.024
Median Absolute Deviation (MAD)9843.6772
Skewness1.8968932
Sum3248017.9
Variance6.9390253 × 108
MonotonicityNot monotonic
2023-12-19T08:28:24.448538image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 14
 
8.7%
130541 1
 
0.6%
9944.701 1
 
0.6%
10916.1 1
 
0.6%
9261.002 1
 
0.6%
8701.2 1
 
0.6%
9593.701 1
 
0.6%
10349.4 1
 
0.6%
10093.1 1
 
0.6%
8749.3 1
 
0.6%
Other values (138) 138
85.7%
ValueCountFrequency (%)
-6860 1
 
0.6%
-6664 1
 
0.6%
-1610 1
 
0.6%
-889 1
 
0.6%
-648 1
 
0.6%
-570 1
 
0.6%
-195 1
 
0.6%
-52 1
 
0.6%
-8 1
 
0.6%
1 14
8.7%
ValueCountFrequency (%)
130541 1
0.6%
120233 1
0.6%
102384 1
0.6%
97843 1
0.6%
91155 1
0.6%
90770 1
0.6%
82487 1
0.6%
81801 1
0.6%
81602 1
0.6%
77344 1
0.6%

Share Holder Equity
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct161
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57178.005
Minimum-8210.3
Maximum256144
Zeros0
Zeros (%)0.0%
Negative11
Negative (%)6.8%
Memory size1.4 KiB
2023-12-19T08:28:24.975935image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum-8210.3
5-th percentile-2204.3
Q112353
median47791
Q391570.7
95-th percentile146043
Maximum256144
Range264354.3
Interquartile range (IQR)79217.7

Descriptive statistics

Standard deviation54098.208
Coefficient of variation (CV)0.94613668
Kurtosis1.4778242
Mean57178.005
Median Absolute Deviation (MAD)38092
Skewness1.135609
Sum9205658.8
Variance2.9266161 × 109
MonotonicityNot monotonic
2023-12-19T08:28:25.361171image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50672 1
 
0.6%
-3824 1
 
0.6%
14033.9 1
 
0.6%
85667.7 1
 
0.6%
96547.15 1
 
0.6%
85876.48 1
 
0.6%
83841.26 1
 
0.6%
85132.21 1
 
0.6%
85081.42 1
 
0.6%
96720.98 1
 
0.6%
Other values (151) 151
93.8%
ValueCountFrequency (%)
-8210.3 1
0.6%
-7824.9 1
0.6%
-6258.4 1
0.6%
-6003.4 1
0.6%
-4601 1
0.6%
-3824 1
0.6%
-3723 1
0.6%
-3268 1
0.6%
-2204.3 1
0.6%
-1956 1
0.6%
ValueCountFrequency (%)
256144 1
0.6%
251635 1
0.6%
222544 1
0.6%
206223 1
0.6%
201442 1
0.6%
177628 1
0.6%
166542 1
0.6%
152502 1
0.6%
146043 1
0.6%
141988 1
0.6%

Cash Flow from Operating
Real number (ℝ)

HIGH CORRELATION 

Distinct160
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20790.318
Minimum-39392.27
Maximum122151
Zeros0
Zeros (%)0.0%
Negative16
Negative (%)9.9%
Memory size1.4 KiB
2023-12-19T08:28:25.696584image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum-39392.27
5-th percentile-1807
Q13502
median7386.7
Q331626
95-th percentile78244.34
Maximum122151
Range161543.27
Interquartile range (IQR)28124

Descriptive statistics

Standard deviation27300.213
Coefficient of variation (CV)1.3131215
Kurtosis1.4293479
Mean20790.318
Median Absolute Deviation (MAD)8046.3
Skewness1.3049089
Sum3347241.2
Variance7.453016 × 108
MonotonicityNot monotonic
2023-12-19T08:28:26.033304image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3502 2
 
1.2%
122151 1
 
0.6%
-1381 1
 
0.6%
5751 1
 
0.6%
37392.72 1
 
0.6%
67268.52 1
 
0.6%
73836.42 1
 
0.6%
-15699.49 1
 
0.6%
11351.14 1
 
0.6%
78244.34 1
 
0.6%
Other values (150) 150
93.2%
ValueCountFrequency (%)
-39392.27 1
0.6%
-21741.23 1
0.6%
-19130 1
0.6%
-17207.81 1
0.6%
-15699.49 1
0.6%
-7818 1
0.6%
-2167 1
0.6%
-1842 1
0.6%
-1807 1
0.6%
-1387 1
0.6%
ValueCountFrequency (%)
122151 1
0.6%
104038 1
0.6%
91652 1
0.6%
91495 1
0.6%
89035 1
0.6%
87582 1
0.6%
81266 1
0.6%
80674 1
0.6%
78244.34 1
0.6%
77434 1
0.6%

Cash Flow from Investing
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct161
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-9581.5332
Minimum-59611
Maximum49749
Zeros0
Zeros (%)0.0%
Negative134
Negative (%)83.2%
Memory size1.4 KiB
2023-12-19T08:28:26.421998image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum-59611
5-th percentile-37601
Q1-17434
median-5904
Q3-981.6
95-th percentile14041
Maximum49749
Range109360
Interquartile range (IQR)16452.4

Descriptive statistics

Standard deviation16196.808
Coefficient of variation (CV)-1.6904192
Kurtosis2.504787
Mean-9581.5332
Median Absolute Deviation (MAD)6231
Skewness-0.18385565
Sum-1542626.9
Variance2.623366 × 108
MonotonicityNot monotonic
2023-12-19T08:28:26.770504image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-22354 1
 
0.6%
244 1
 
0.6%
-1655.3 1
 
0.6%
-26807.33 1
 
0.6%
5871.677 1
 
0.6%
-23594.78 1
 
0.6%
-16377.52 1
 
0.6%
903.6596 1
 
0.6%
4513.377 1
 
0.6%
49749 1
 
0.6%
Other values (151) 151
93.8%
ValueCountFrequency (%)
-59611 1
0.6%
-58154 1
0.6%
-56274 1
0.6%
-48227 1
0.6%
-46781 1
0.6%
-46446 1
0.6%
-45977 1
0.6%
-40419 1
0.6%
-37601 1
0.6%
-35523 1
0.6%
ValueCountFrequency (%)
49749 1
0.6%
45896 1
0.6%
36448 1
0.6%
18026.64 1
0.6%
17560.51 1
0.6%
16612 1
0.6%
16066 1
0.6%
14284 1
0.6%
14041 1
0.6%
8462 1
0.6%

Cash Flow from Financial Activities
Real number (ℝ)

HIGH CORRELATION 

Distinct160
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-8598.1579
Minimum-110749
Maximum25928
Zeros0
Zeros (%)0.0%
Negative106
Negative (%)65.8%
Memory size1.4 KiB
2023-12-19T08:28:27.180460image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum-110749
5-th percentile-48486
Q1-9059.288
median-1408
Q3735.3
95-th percentile6291
Maximum25928
Range136677
Interquartile range (IQR)9794.588

Descriptive statistics

Standard deviation20281.663
Coefficient of variation (CV)-2.3588381
Kurtosis9.3414559
Mean-8598.1579
Median Absolute Deviation (MAD)3586.8
Skewness-2.9056195
Sum-1384303.4
Variance4.1134585 × 108
MonotonicityNot monotonic
2023-12-19T08:28:27.547898image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-676 2
 
1.2%
-110749 1
 
0.6%
1185 1
 
0.6%
-4421 1
 
0.6%
860.8824 1
 
0.6%
147.1357 1
 
0.6%
3507.888 1
 
0.6%
881.061 1
 
0.6%
-9059.288 1
 
0.6%
1238.537 1
 
0.6%
Other values (150) 150
93.2%
ValueCountFrequency (%)
-110749 1
0.6%
-93353 1
0.6%
-90976 1
0.6%
-87876 1
0.6%
-86820 1
0.6%
-69757 1
0.6%
-61362 1
0.6%
-58876 1
0.6%
-48486 1
0.6%
-46031 1
0.6%
ValueCountFrequency (%)
25928 1
0.6%
12454 1
0.6%
9928 1
0.6%
9718 1
0.6%
9247.968 1
0.6%
8408 1
0.6%
7258 1
0.6%
7133 1
0.6%
6291 1
0.6%
5058 1
0.6%

Current Ratio
Real number (ℝ)

HIGH CORRELATION 

Distinct134
Distinct (%)83.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0353683
Minimum0.2205
Maximum10.6178
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-19T08:28:27.928497image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0.2205
5-th percentile0.8186
Q11
median1.3412
Q32.4734
95-th percentile5.9192
Maximum10.6178
Range10.3973
Interquartile range (IQR)1.4734

Descriptive statistics

Standard deviation1.6608936
Coefficient of variation (CV)0.81601623
Kurtosis6.422918
Mean2.0353683
Median Absolute Deviation (MAD)0.3412
Skewness2.3589763
Sum327.6943
Variance2.7585675
MonotonicityNot monotonic
2023-12-19T08:28:28.292985image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 28
 
17.4%
0.8794 1
 
0.6%
3.5156 1
 
0.6%
1.5931 1
 
0.6%
1.4464 1
 
0.6%
1.2547 1
 
0.6%
1.4937 1
 
0.6%
1.1431 1
 
0.6%
6.6503 1
 
0.6%
3.2684 1
 
0.6%
Other values (124) 124
77.0%
ValueCountFrequency (%)
0.2205 1
0.6%
0.6356 1
0.6%
0.707 1
0.6%
0.7713 1
0.6%
0.7756 1
0.6%
0.7977 1
0.6%
0.8117 1
0.6%
0.8149 1
0.6%
0.8186 1
0.6%
0.8303 1
0.6%
ValueCountFrequency (%)
10.6178 1
0.6%
8.0269 1
0.6%
7.9436 1
0.6%
7.6738 1
0.6%
6.6503 1
0.6%
6.3761 1
0.6%
6.2908 1
0.6%
5.949 1
0.6%
5.9192 1
0.6%
5.1403 1
0.6%

Debt/Equity Ratio
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct152
Distinct (%)94.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6455205
Minimum-11.775
Maximum9.3328
Zeros8
Zeros (%)5.0%
Negative11
Negative (%)6.8%
Memory size1.4 KiB
2023-12-19T08:28:28.674499image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum-11.775
5-th percentile-1.5215
Q10.0772
median0.3818
Q30.8914
95-th percentile5.8975
Maximum9.3328
Range21.1078
Interquartile range (IQR)0.8142

Descriptive statistics

Standard deviation2.5071768
Coefficient of variation (CV)3.8839616
Kurtosis7.4540249
Mean0.6455205
Median Absolute Deviation (MAD)0.339
Skewness-0.43981179
Sum103.9288
Variance6.2859358
MonotonicityNot monotonic
2023-12-19T08:28:29.029204image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 8
 
5.0%
0.3164 2
 
1.2%
0.0226 2
 
1.2%
7.688 1
 
0.6%
5.3348 1
 
0.6%
5.8975 1
 
0.6%
5.2797 1
 
0.6%
3.5002 1
 
0.6%
3.6646 1
 
0.6%
5.0612 1
 
0.6%
Other values (142) 142
88.2%
ValueCountFrequency (%)
-11.775 1
0.6%
-9.0381 1
0.6%
-7.7424 1
0.6%
-5.9805 1
0.6%
-4.9654 1
0.6%
-4.7848 1
0.6%
-4.1627 1
0.6%
-3.9958 1
0.6%
-1.5215 1
0.6%
-1.1099 1
0.6%
ValueCountFrequency (%)
9.3328 1
0.6%
8.5152 1
0.6%
7.688 1
0.6%
7.6045 1
0.6%
7.1602 1
0.6%
6.6208 1
0.6%
6.3396 1
0.6%
6.0327 1
0.6%
5.8975 1
0.6%
5.3348 1
0.6%

ROE
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct161
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.432402
Minimum-212.6069
Maximum196.9589
Zeros0
Zeros (%)0.0%
Negative24
Negative (%)14.9%
Memory size1.4 KiB
2023-12-19T08:28:29.438756image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum-212.6069
5-th percentile-60.4545
Q15.08
median14.5016
Q328.1893
95-th percentile55.5601
Maximum196.9589
Range409.5658
Interquartile range (IQR)23.1093

Descriptive statistics

Standard deviation44.777292
Coefficient of variation (CV)3.6016606
Kurtosis9.9687015
Mean12.432402
Median Absolute Deviation (MAD)11.2875
Skewness-1.0428999
Sum2001.6167
Variance2005.0059
MonotonicityNot monotonic
2023-12-19T08:28:29.789362image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
196.9589 1
 
0.6%
58.0805 1
 
0.6%
32.4286 1
 
0.6%
8.624 1
 
0.6%
10.2918 1
 
0.6%
3.6796 1
 
0.6%
5.1081 1
 
0.6%
3.7191 1
 
0.6%
1.9707 1
 
0.6%
3.1346 1
 
0.6%
Other values (151) 151
93.8%
ValueCountFrequency (%)
-212.6069 1
0.6%
-163.9903 1
0.6%
-158.8832 1
0.6%
-141.8337 1
0.6%
-102.8983 1
0.6%
-94.6616 1
0.6%
-73.3883 1
0.6%
-71.8728 1
0.6%
-60.4545 1
0.6%
-52.9877 1
0.6%
ValueCountFrequency (%)
196.9589 1
0.6%
191.5344 1
0.6%
150.0713 1
0.6%
87.8664 1
0.6%
63.9019 1
0.6%
61.0645 1
0.6%
58.0805 1
0.6%
57.6687 1
0.6%
55.5601 1
0.6%
44.7355 1
0.6%

ROA
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct161
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.7804789
Minimum-23.7236
Maximum31.1541
Zeros0
Zeros (%)0.0%
Negative21
Negative (%)13.0%
Memory size1.4 KiB
2023-12-19T08:28:30.147042image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum-23.7236
5-th percentile-5.274
Q10.8282
median6.6411
Q314.7016
95-th percentile21.2964
Maximum31.1541
Range54.8777
Interquartile range (IQR)13.8734

Descriptive statistics

Standard deviation8.8024623
Coefficient of variation (CV)1.1313522
Kurtosis0.41289395
Mean7.7804789
Median Absolute Deviation (MAD)6.4056
Skewness-0.13272698
Sum1252.6571
Variance77.483342
MonotonicityNot monotonic
2023-12-19T08:28:30.503403image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28.2924 1
 
0.6%
-23.7236 1
 
0.6%
15.0571 1
 
0.6%
0.3946 1
 
0.6%
0.522 1
 
0.6%
0.1824 1
 
0.6%
0.2942 1
 
0.6%
0.2093 1
 
0.6%
0.1148 1
 
0.6%
0.1844 1
 
0.6%
Other values (151) 151
93.8%
ValueCountFrequency (%)
-23.7236 1
0.6%
-14.5924 1
0.6%
-13.7277 1
0.6%
-9.9497 1
0.6%
-8.9699 1
0.6%
-8.8798 1
0.6%
-6.1114 1
0.6%
-5.4498 1
0.6%
-5.274 1
0.6%
-1.8959 1
0.6%
ValueCountFrequency (%)
31.1541 1
0.6%
28.2924 1
0.6%
27.1061 1
0.6%
26.9742 1
0.6%
23.7033 1
0.6%
22.2753 1
0.6%
22.0698 1
0.6%
21.7853 1
0.6%
21.2964 1
0.6%
21.1633 1
0.6%

ROI
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct161
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.88135
Minimum-742.1052
Maximum884.8605
Zeros0
Zeros (%)0.0%
Negative19
Negative (%)11.8%
Memory size1.4 KiB
2023-12-19T08:28:30.876320image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum-742.1052
5-th percentile-6.0641
Q12.7579
median13.4694
Q320.4723
95-th percentile33.8341
Maximum884.8605
Range1626.9657
Interquartile range (IQR)17.7144

Descriptive statistics

Standard deviation93.384692
Coefficient of variation (CV)7.859771
Kurtosis74.131489
Mean11.88135
Median Absolute Deviation (MAD)8.7878
Skewness1.8023083
Sum1912.8974
Variance8720.7007
MonotonicityNot monotonic
2023-12-19T08:28:31.204688image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
66.6994 1
 
0.6%
884.8605 1
 
0.6%
18.5044 1
 
0.6%
1.284 1
 
0.6%
1.6696 1
 
0.6%
0.602 1
 
0.6%
0.9201 1
 
0.6%
0.6186 1
 
0.6%
0.3862 1
 
0.6%
0.8387 1
 
0.6%
Other values (151) 151
93.8%
ValueCountFrequency (%)
-742.1052 1
0.6%
-141.8337 1
0.6%
-84.5005 1
0.6%
-52.9877 1
0.6%
-48.5301 1
0.6%
-22.2444 1
0.6%
-20.6061 1
0.6%
-6.2251 1
0.6%
-6.0641 1
0.6%
-5.5694 1
0.6%
ValueCountFrequency (%)
884.8605 1
0.6%
66.6994 1
0.6%
54.9839 1
0.6%
36.7023 1
0.6%
36.549 1
0.6%
35.3041 1
0.6%
35.0054 1
0.6%
34.0575 1
0.6%
33.8341 1
0.6%
33.6435 1
0.6%

Net Profit Margin
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct161
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.684964
Minimum-44.6961
Maximum36.6863
Zeros0
Zeros (%)0.0%
Negative25
Negative (%)15.5%
Memory size1.4 KiB
2023-12-19T08:28:31.584457image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum-44.6961
5-th percentile-7.1363
Q14.8277
median15.2977
Q322.9345
95-th percentile31.1817
Maximum36.6863
Range81.3824
Interquartile range (IQR)18.1068

Descriptive statistics

Standard deviation13.401767
Coefficient of variation (CV)0.97930597
Kurtosis2.4764543
Mean13.684964
Median Absolute Deviation (MAD)9.6334
Skewness-1.0463306
Sum2203.2792
Variance179.60736
MonotonicityNot monotonic
2023-12-19T08:28:31.942321image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25.3096 1
 
0.6%
-10.0325 1
 
0.6%
20.0091 1
 
0.6%
21.162 1
 
0.6%
28.2167 1
 
0.6%
9.0147 1
 
0.6%
16.6024 1
 
0.6%
7.0877 1
 
0.6%
-10.2561 1
 
0.6%
9.1781 1
 
0.6%
Other values (151) 151
93.8%
ValueCountFrequency (%)
-44.6961 1
0.6%
-40.8795 1
0.6%
-16.2286 1
0.6%
-13.6569 1
0.6%
-12.2859 1
0.6%
-10.2561 1
0.6%
-10.0325 1
0.6%
-7.5541 1
0.6%
-7.1363 1
0.6%
-5.3914 1
0.6%
ValueCountFrequency (%)
36.6863 1
0.6%
36.4517 1
0.6%
36.2339 1
0.6%
35.3448 1
0.6%
34.1462 1
0.6%
33.0984 1
0.6%
32.4903 1
0.6%
31.3671 1
0.6%
31.1817 1
0.6%
30.9625 1
0.6%

Free Cash Flow per Share
Real number (ℝ)

Distinct160
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.19282547
Minimum-121.5022
Maximum137.3287
Zeros0
Zeros (%)0.0%
Negative60
Negative (%)37.3%
Memory size1.4 KiB
2023-12-19T08:28:32.337914image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum-121.5022
5-th percentile-11.611
Q1-0.3613
median0.2656
Q30.9354
95-th percentile6.6557
Maximum137.3287
Range258.8309
Interquartile range (IQR)1.2967

Descriptive statistics

Standard deviation15.412788
Coefficient of variation (CV)79.931289
Kurtosis62.969446
Mean0.19282547
Median Absolute Deviation (MAD)0.6278
Skewness1.3377452
Sum31.0449
Variance237.55404
MonotonicityNot monotonic
2023-12-19T08:28:32.736178image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.3622 2
 
1.2%
1.3146 1
 
0.6%
1.3378 1
 
0.6%
0.463 1
 
0.6%
3.4305 1
 
0.6%
-6.5477 1
 
0.6%
-1.4283 1
 
0.6%
20.4341 1
 
0.6%
-6.2895 1
 
0.6%
-15.5712 1
 
0.6%
Other values (150) 150
93.2%
ValueCountFrequency (%)
-121.5022 1
0.6%
-21.998 1
0.6%
-18.5013 1
0.6%
-15.8715 1
0.6%
-15.5712 1
0.6%
-13.9382 1
0.6%
-12.8733 1
0.6%
-12.4078 1
0.6%
-11.611 1
0.6%
-7.424 1
0.6%
ValueCountFrequency (%)
137.3287 1
0.6%
21.5535 1
0.6%
20.4341 1
0.6%
18.6025 1
0.6%
14.9014 1
0.6%
14.5974 1
0.6%
9.9996 1
0.6%
7.9685 1
0.6%
6.6557 1
0.6%
6.1199 1
0.6%

Return on Tangible Equity
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct161
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.247542
Minimum-554.1741
Maximum1197.727
Zeros0
Zeros (%)0.0%
Negative22
Negative (%)13.7%
Memory size1.4 KiB
2023-12-19T08:28:33.100180image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum-554.1741
5-th percentile-52.9877
Q16.1118
median19.7822
Q336.9806
95-th percentile72.5298
Maximum1197.727
Range1751.9011
Interquartile range (IQR)30.8688

Descriptive statistics

Standard deviation109.98282
Coefficient of variation (CV)4.5358337
Kurtosis86.135051
Mean24.247542
Median Absolute Deviation (MAD)14.9511
Skewness6.7459157
Sum3903.8542
Variance12096.22
MonotonicityNot monotonic
2023-12-19T08:28:33.443249image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
196.9589 1
 
0.6%
39.5618 1
 
0.6%
39.2034 1
 
0.6%
9.7884 1
 
0.6%
11.6267 1
 
0.6%
4.1759 1
 
0.6%
5.8289 1
 
0.6%
4.2504 1
 
0.6%
2.2367 1
 
0.6%
3.5151 1
 
0.6%
Other values (151) 151
93.8%
ValueCountFrequency (%)
-554.1741 1
0.6%
-141.8337 1
0.6%
-103.2087 1
0.6%
-102.19 1
0.6%
-91.9365 1
0.6%
-69.3794 1
0.6%
-68.9682 1
0.6%
-55.3414 1
0.6%
-52.9877 1
0.6%
-44.6358 1
0.6%
ValueCountFrequency (%)
1197.727 1
0.6%
196.9589 1
0.6%
150.0713 1
0.6%
106.6922 1
0.6%
99.0748 1
0.6%
87.8664 1
0.6%
82.9207 1
0.6%
74.6661 1
0.6%
72.5298 1
0.6%
65.2006 1
0.6%

Number of Employees
Real number (ℝ)

HIGH CORRELATION 

Distinct149
Distinct (%)92.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean145718.77
Minimum5420
Maximum1608000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-19T08:28:33.820177image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum5420
5-th percentile10299
Q129900
median96000
Q3147000
95-th percentile420000
Maximum1608000
Range1602580
Interquartile range (IQR)117100

Descriptive statistics

Standard deviation223438.9
Coefficient of variation (CV)1.533357
Kurtosis24.172523
Mean145718.77
Median Absolute Deviation (MAD)59400
Skewness4.4920906
Sum23460722
Variance4.992494 × 1010
MonotonicityNot monotonic
2023-12-19T08:28:34.178056image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23000 3
 
1.9%
420000 3
 
1.9%
24000 3
 
1.9%
221000 2
 
1.2%
128000 2
 
1.2%
118000 2
 
1.2%
200000 2
 
1.2%
63000 2
 
1.2%
440000 2
 
1.2%
129400 1
 
0.6%
Other values (139) 139
86.3%
ValueCountFrequency (%)
5420 1
0.6%
5706 1
0.6%
6029 1
0.6%
7133 1
0.6%
7974 1
0.6%
8808 1
0.6%
9227 1
0.6%
9228 1
0.6%
10299 1
0.6%
11528 1
0.6%
ValueCountFrequency (%)
1608000 1
 
0.6%
1541000 1
 
0.6%
1298000 1
 
0.6%
798000 1
 
0.6%
647500 1
 
0.6%
566000 1
 
0.6%
440000 2
1.2%
420000 3
1.9%
400000 1
 
0.6%
385000 1
 
0.6%

Inflation Rate(in US)
Real number (ℝ)

Distinct15
Distinct (%)9.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2313174
Minimum-0.3555
Maximum8.0028
Zeros0
Zeros (%)0.0%
Negative11
Negative (%)6.8%
Memory size1.4 KiB
2023-12-19T08:28:34.537086image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum-0.3555
5-th percentile-0.3555
Q11.2616
median1.8122
Q32.4426
95-th percentile8.0028
Maximum8.0028
Range8.3583
Interquartile range (IQR)1.181

Descriptive statistics

Standard deviation1.9591392
Coefficient of variation (CV)0.87801905
Kurtosis2.8406593
Mean2.2313174
Median Absolute Deviation (MAD)0.5786
Skewness1.6484883
Sum359.2421
Variance3.8382263
MonotonicityNot monotonic
2023-12-19T08:28:34.842901image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
2.4426 12
 
7.5%
2.1301 12
 
7.5%
1.2616 12
 
7.5%
0.1186 12
 
7.5%
1.6222 12
 
7.5%
8.0028 11
 
6.8%
4.6979 11
 
6.8%
1.2336 11
 
6.8%
1.8122 11
 
6.8%
1.4648 11
 
6.8%
Other values (5) 46
28.6%
ValueCountFrequency (%)
-0.3555 11
6.8%
0.1186 12
7.5%
1.2336 11
6.8%
1.2616 12
7.5%
1.4648 11
6.8%
1.6222 12
7.5%
1.64 11
6.8%
1.8122 11
6.8%
2.0693 11
6.8%
2.1301 12
7.5%
ValueCountFrequency (%)
8.0028 11
6.8%
4.6979 11
6.8%
3.7 2
 
1.2%
3.1568 11
6.8%
2.4426 12
7.5%
2.1301 12
7.5%
2.0693 11
6.8%
1.8122 11
6.8%
1.64 11
6.8%
1.6222 12
7.5%

Interactions

2023-12-19T08:28:08.353664image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:25:44.159757image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:25:51.679356image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:25:58.650450image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:26:05.420643image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:26:10.862466image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:26:20.338128image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:26:34.662326image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:26:40.400164image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:26:47.313139image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:26:54.823609image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:27:01.478092image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
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2023-12-19T08:27:13.729502image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:27:19.884613image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
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2023-12-19T08:28:02.343101image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:28:09.125903image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
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2023-12-19T08:25:52.262822image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:25:59.184808image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
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2023-12-19T08:26:40.966683image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:26:47.962930image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:26:55.366886image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
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2023-12-19T08:25:45.072009image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
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2023-12-19T08:28:05.939498image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:28:13.481786image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:25:49.412490image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:25:56.926049image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:26:03.288986image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:26:09.242520image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:26:15.860437image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:26:29.990963image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:26:38.732177image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:26:44.945219image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:26:53.170084image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:26:59.252121image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:27:05.604896image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:27:11.221958image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:27:18.210446image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:27:23.948433image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:27:30.532288image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:27:36.139572image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:27:43.536869image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:27:53.442520image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:28:00.374683image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:28:06.204348image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:28:13.760346image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:25:49.893686image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:25:57.200099image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:26:03.714797image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:26:09.510191image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:26:16.794817image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
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2023-12-19T08:26:39.003414image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:26:45.355790image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:26:53.442123image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:26:59.623732image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:27:05.896668image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:27:11.645218image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:27:18.497478image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:27:24.224008image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:27:30.811462image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:27:36.443544image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:27:43.842659image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:27:53.911361image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:28:00.648570image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
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2023-12-19T08:26:09.836486image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:26:17.537938image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:26:31.795397image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:26:39.284635image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:26:45.750064image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:26:53.722640image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:27:00.038032image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:27:06.183286image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:27:12.046618image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:27:18.794344image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:27:24.516533image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:27:31.071338image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:27:36.745853image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:27:44.134316image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:27:54.351944image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:28:00.930181image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:28:06.785163image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:28:14.312875image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:25:50.766443image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:25:57.803628image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:26:04.498104image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
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2023-12-19T08:26:18.180420image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:26:32.633181image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:26:39.554352image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:26:46.107384image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:26:53.983887image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:27:00.449915image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:27:06.457636image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:27:12.413652image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:27:19.061440image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:27:24.784971image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:27:31.322306image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:27:37.036723image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:27:44.418719image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
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2023-12-19T08:28:01.213684image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:28:07.054918image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:28:14.602570image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:25:51.085589image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:25:58.100872image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
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2023-12-19T08:26:33.554462image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:26:39.842626image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
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2023-12-19T08:26:54.272601image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
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2023-12-19T08:27:06.749372image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:27:12.839301image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:27:19.341126image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
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2023-12-19T08:27:31.575983image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:27:37.334933image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:27:44.714546image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:27:55.114452image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:28:01.482229image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:28:07.484371image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:28:14.911512image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:25:51.405844image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:25:58.388154image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:26:05.176794image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:26:10.637486image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:26:19.947424image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:26:34.182353image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:26:40.143566image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:26:46.941033image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:26:54.565545image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:27:01.211912image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:27:07.032396image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:27:13.319559image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:27:19.625495image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:27:25.423793image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:27:31.857830image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:27:37.642344image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:27:45.017248image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:27:55.527978image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:28:01.780919image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-12-19T08:28:07.957227image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Correlations

2023-12-19T08:28:35.292587image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
YearMarket Cap(in B USD)RevenueGross ProfitNet IncomeEarning Per ShareEBITDAShare Holder EquityCash Flow from OperatingCash Flow from InvestingCash Flow from Financial ActivitiesCurrent RatioDebt/Equity RatioROEROAROINet Profit MarginFree Cash Flow per ShareReturn on Tangible EquityNumber of EmployeesInflation Rate(in US)CompanyCategory
Year1.0000.3610.1640.2380.2180.2570.2360.1980.254-0.194-0.077-0.0690.0970.0580.0630.1360.194-0.0310.1010.1110.4380.0000.000
Market Cap(in B USD)0.3611.0000.6910.7290.8000.4230.8180.5120.754-0.611-0.4780.438-0.1690.5240.6960.6630.6270.0880.5170.3540.1530.2310.210
Revenue0.1640.6911.0000.8820.6600.2540.7800.7410.720-0.541-0.5530.0420.0890.3710.2790.2930.2390.0580.3580.5680.1020.3360.269
Gross Profit0.2380.7290.8821.0000.7370.3210.7340.7890.837-0.665-0.5050.0700.1780.3600.3550.3400.3760.0750.3410.5130.1680.3720.299
Net Income0.2180.8000.6600.7371.0000.6780.8460.5970.800-0.582-0.6040.408-0.1290.6590.7740.7520.8070.0790.6210.2940.1790.3550.294
Earning Per Share0.2570.4230.2540.3210.6781.0000.5690.2400.450-0.177-0.4820.0960.0320.4200.5760.5640.6620.0790.3860.2830.2250.3480.344
EBITDA0.2360.8180.7800.7340.8460.5691.0000.6010.740-0.601-0.6010.304-0.1360.5380.6670.6360.5900.1240.4940.3240.1440.4220.332
Share Holder Equity0.1980.5120.7410.7890.5970.2400.6011.0000.658-0.527-0.4290.0100.2460.2720.1770.1310.2690.0190.2040.1980.1020.3500.308
Cash Flow from Operating0.2540.7540.7200.8370.8000.4500.7400.6581.000-0.603-0.5190.2270.0530.4800.5480.5170.5500.2040.4460.4100.1360.3240.259
Cash Flow from Investing-0.194-0.611-0.541-0.665-0.582-0.177-0.601-0.527-0.6031.0000.070-0.2470.083-0.337-0.396-0.348-0.3740.008-0.319-0.161-0.0780.3380.274
Cash Flow from Financial Activities-0.077-0.478-0.553-0.505-0.604-0.482-0.601-0.429-0.5190.0701.000-0.1850.064-0.376-0.467-0.465-0.433-0.154-0.342-0.376-0.0490.3030.211
Current Ratio-0.0690.4380.0420.0700.4080.0960.3040.0100.227-0.247-0.1851.000-0.5520.4140.6190.5000.5630.0150.429-0.119-0.0440.4050.313
Debt/Equity Ratio0.097-0.1690.0890.178-0.1290.032-0.1360.2460.0530.0830.064-0.5521.000-0.053-0.323-0.365-0.2610.0050.0110.1430.0420.4020.392
ROE0.0580.5240.3710.3600.6590.4200.5380.2720.480-0.337-0.3760.414-0.0531.0000.6950.7340.5840.1120.9030.1420.0640.2660.263
ROA0.0630.6960.2790.3550.7740.5760.6670.1770.548-0.396-0.4670.619-0.3230.6951.0000.9140.8690.1540.6480.1100.1240.4090.461
ROI0.1360.6630.2930.3400.7520.5640.6360.1310.517-0.348-0.4650.500-0.3650.7340.9141.0000.8080.1430.6680.1700.1670.1290.205
Net Profit Margin0.1940.6270.2390.3760.8070.6620.5900.2690.550-0.374-0.4330.563-0.2610.5840.8690.8081.0000.0950.5590.0650.2130.4640.439
Free Cash Flow per Share-0.0310.0880.0580.0750.0790.0790.1240.0190.2040.008-0.1540.0150.0050.1120.1540.1430.0951.0000.1300.064-0.1440.1460.109
Return on Tangible Equity0.1010.5170.3580.3410.6210.3860.4940.2040.446-0.319-0.3420.4290.0110.9030.6480.6680.5590.1301.0000.1980.0800.3320.333
Number of Employees0.1110.3540.5680.5130.2940.2830.3240.1980.410-0.161-0.376-0.1190.1430.1420.1100.1700.0650.0640.1981.0000.0460.4070.434
Inflation Rate(in US)0.4380.1530.1020.1680.1790.2250.1440.1020.136-0.078-0.049-0.0440.0420.0640.1240.1670.213-0.1440.0800.0461.0000.0000.000
Company0.0000.2310.3360.3720.3550.3480.4220.3500.3240.3380.3030.4050.4020.2660.4090.1290.4640.1460.3320.4070.0001.0000.987
Category0.0000.2100.2690.2990.2940.3440.3320.3080.2590.2740.2110.3130.3920.2630.4610.2050.4390.1090.3330.4340.0000.9871.000

Missing values

2023-12-19T08:28:15.354308image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-19T08:28:16.187769image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

YearCompanyCategoryMarket Cap(in B USD)RevenueGross ProfitNet IncomeEarning Per ShareEBITDAShare Holder EquityCash Flow from OperatingCash Flow from InvestingCash Flow from Financial ActivitiesCurrent RatioDebt/Equity RatioROEROAROINet Profit MarginFree Cash Flow per ShareReturn on Tangible EquityNumber of EmployeesInflation Rate(in US)
02022AAPLIT2066.94394328.0170782.099803.06.1100130541.050672.0122151.0-22354.0-110749.00.87942.3695196.958928.292466.699425.30961.3146196.95891640008.0028
12021AAPLIT2913.28365817.0152836.094680.05.6100120233.063090.0104038.0-14545.0-93353.01.07461.9768150.071326.974254.983925.88181.3261150.07131540004.6979
22020AAPLIT2255.97274515.0104956.057411.03.280077344.065339.080674.0-4289.0-86820.01.36361.720887.866417.725635.005420.91361.018387.86641470001.2336
32019AAPLIT1304.76260174.098392.055256.02.970076477.090488.069391.045896.0-90976.01.54011.194061.064516.323030.311321.2381-0.038861.06451370001.8122
42018AAPLIT748.54265595.0101839.059531.02.980081801.0107147.077434.016066.0-87876.01.13291.068555.560116.277529.634822.41420.741455.56011320002.4426
52017AAPLIT868.87229234.088186.048351.02.302571501.0134047.064225.0-46446.0-17974.01.27610.863036.070212.882620.908221.09240.033036.07021230002.1301
62016AAPLIT617.59215639.084263.045687.02.077570529.0128249.066231.0-45977.0-20890.01.35270.678635.623714.202422.431221.1868-0.590138.19061160001.2616
72015AAPLIT586.86233715.093626.053394.02.305082487.0119355.081266.0-56274.0-17716.01.10880.539044.735518.389930.920122.84580.974348.38781100000.1186
82014AAPLIT647.36182795.070537.039510.01.612560449.0111547.059713.0-22579.0-37549.01.08010.316435.420117.042028.114221.61440.303238.4380970001.6222
92013AAPLIT504.79170910.064304.037037.01.420055756.0123549.053666.0-33774.0-16379.01.67860.137329.977617.892326.359221.67050.136331.4425844001.4648
YearCompanyCategoryMarket Cap(in B USD)RevenueGross ProfitNet IncomeEarning Per ShareEBITDAShare Holder EquityCash Flow from OperatingCash Flow from InvestingCash Flow from Financial ActivitiesCurrent RatioDebt/Equity RatioROEROAROINet Profit MarginFree Cash Flow per ShareReturn on Tangible EquityNumber of EmployeesInflation Rate(in US)
1512018AMZNLOGI734.42232887.093731.010073.01.007027762.043549.030723.0-12369.0-7686.01.09810.539523.13036.193115.02454.32531.097534.73336475002.4426
1522017AMZNLOGI563.54177866.065932.03033.00.307515584.027709.018365.0-27084.09928.01.04000.893010.94592.30985.78241.7052-0.238721.12265660002.1301
1532016AMZNLOGI356.31135987.047722.02371.00.245012302.019285.017203.0-9516.0-3716.01.04480.399012.29452.84298.78831.74350.300315.29583414001.2616
1542015AMZNLOGI316.83107006.035355.0596.00.06258514.013384.012039.0-6450.0-3882.01.05360.61474.45310.92052.75790.55700.57006.19222308000.1186
1552014AMZNLOGI143.7088988.026236.0-241.0-0.02604924.010741.06842.0-5065.04432.01.11530.7695-2.2437-0.4422-1.2680-0.2708-0.0075-3.24711541001.6222
1562013AMZNLOGI182.5474452.020271.0274.00.02953998.09746.05475.0-4276.0-539.01.07160.32742.81140.68232.11800.36800.17483.86411173001.4648
1572012AMZNLOGI113.6361093.015122.0-39.0-0.00452835.08192.04180.0-3595.02259.01.12070.3765-0.4761-0.1198-0.3459-0.0638-0.1833-0.6915884002.0693
1582011AMZNLOGI78.7248077.010789.0631.00.06851945.07757.03903.0-1930.0-482.01.17410.03298.13462.49627.87571.3125-0.049010.8756562003.1568
1592010AMZNLOGI80.7934204.07643.01152.00.12651974.06864.03495.0-3360.0181.01.32540.227416.78326.128613.67363.3680-0.054420.8885337001.6400
1602009AMZNLOGI58.2424509.05531.0902.00.10201507.05257.03293.0-2337.0-280.01.33040.200717.15816.530117.15813.68030.330322.421124300-0.3555